Comparison of stochastic and deterministic methods for mapping groundwater level spatial variability in sparsely monitored basins

In sparsely monitored basins, accurate mapping of the spatial variability of groundwater level requires the interpolation of scattered data. This paper presents a comparison of deterministic interpolation methods, i.e. inverse distance weight (IDW) and minimum curvature (MC), with stochastic methods, i.e. ordinary kriging (OK), universal kriging (UK) and kriging with Delaunay triangulation (DK). The study area is the Mires Basin of Mesara Valley in Crete (Greece). This sparsely sampled basin has limited groundwater resources which are vital for the island’s economy; spatial variations of the groundwater level are important for developing management and monitoring strategies. We evaluate the performance of the interpolation methods with respect to different statistical measures. The Spartan variogram family is applied for the first time to hydrological data and is shown to be optimal with respect to stochastic interpolation of this dataset. The three stochastic methods (OK, DK and UK) perform overall better than the deterministic counterparts (IDW and MC). DK, which is herein for the first time applied to hydrological data, yields the most accurate cross-validation estimate for the lowest value in the dataset. OK and UK lead to smooth isolevel contours, whilst DK and IDW generate more edges. The stochastic methods deliver estimates of prediction uncertainty which becomes highest near the southeastern border of the basin.     

山区土壤重金属空间插值性能的改进

山区土壤重金属监测中,密集均匀的布点采样困难大,成本高。根据稀疏非均匀样本数据准确预测山区土壤重金属空间分布,对可视化描述检测元素的分布及趋势具有重要意义。基于云南省楚雄市南部山区表层土壤中重金属锰、钒的采样数据,对集成径向基函数神经网络(IRBFANNs)和传统插值方法:反距离(IDW)、普通克里格(OK)、径向基函数神经网络(RBFANNs),进行了不同等级采样密度下的插值性能比较。结果表明,样本点数量减少时,传统预测方法的插值精度都有所下降,而IRBFANNs算法在样本点较少情况下能够保持最好的插值精确度和稳定性,有效改进了空间插值性能。     

土壤重金属污染空间插值及其验证方法研究综述

综述了土壤重金属污染空间插值方法研究现状,分析了3类典型的插值方法(确定性插值方法、地统计学插值方法和组合插值方法),以及2种常用的精度验证方法(交叉验证法和独立的数据集验证法)各自的优缺点与适用范围,提出了加强组合插值模型优化与精度提升、利用计算机智能技术优化插值算法模型、开发针对土壤重金属领域的考虑空间插值软数据挖...     

Estimation of spatial distrubition of groundwater level and risky areas of seawater intrusion on the coastal region in Çarşamba Plain,Turkey, using different interpolation methods

Groundwater level plays a significant role in coastal plains. Heavy pumping and excessive use of near-coast groundwater can increase the intrusion of seawater into the aquifers. In the present study, groundwater levels were measured at 59 groundwater wells at different times during pre- and post-irrigation seasons (April and September of the year 2012) in Çar?amba Plain, Turkey. To select the best method, two deterministic interpolation methods (inverse distance weighing (IDW) with the weights of 1, 2, and 3 and radial basis function (RBF) with spline with tension (SPT) and completely regularized spline (CRS)) and two stochastic methods (ordinary kriging (OK) with spherical, exponential, and Gaussian variograms) and cokriging (COK)) were compared and then the best interpolation method was used to evaluate the spatial distribution of groundwater levels in different seasons and seasonal changes. A total of nine different techniques were tested. Also, risky areas of seawater intrusion in coastal area were determined using the best methods for two periods. The performance of these interpolation methods is evaluated by using a validation test method. Statistical indices of correlation (R ²), mean absolute error (MAE), and root-mean-square error (RMSE) were used to select and validate the best methods. Comparisons between predicted and observed values indicated RBF as the optimal method for groundwater level estimation in April and September. When the best method RBF and the worst method IDW were compared, significant differences were observed in the spatial distribution of groundwater. Results of the study also revealed that excessive groundwater withdrawals during the post-irrigation season dropped the groundwater levels up to 2.0 m in some sections. With regard to seawater intrusion, 9,103 ha of land area was determined to be highly risky and risky.     

Assessing uncertainty in annual nitrogen,phosphorus, and suspended sediment load estimates in three agricultural streams using a 21-year dataset

Accurate estimation of constituent loads is important for studies of ecosystem mass balance or total maximum daily loads. In response, there has been an effort to develop methods to increase both accuracy and precision of constituent load estimates. The relationship between constituent concentration and stream discharge is often complicated, potentially leading to high uncertainty in load estimates for certain constituents, especially at longer-term (annual) scales. We used the loadflex R package to compare uncertainty in annual load estimates from concentration vs. discharge relationships in constituents of interest in agricultural systems, including ammonium as nitrogen (NH₄-N), nitrate as nitrogen (NO₃-N), soluble reactive phosphorus (SRP), and suspended sediments (SS). We predicted that uncertainty would be greatest in NO₃-N and SS due to complex relationships between constituent concentration and discharge. We also predicted lower uncertainty with a composite method compared to regression or interpolation methods. Contrary to predictions, we observed the lowest uncertainty in annual NO₃-N load estimates (relative error 1.5–23%); however, uncertainty was greatest in SS load estimates, consistent with predictions (relative error 19–96%). For all constituents, we also generally observed reductions in uncertainty by up to 34% using the composite method compared to regression and interpolation approaches, as predicted. These results highlight differences in uncertainty among different constituents and will aid in model selection for future studies requiring accurate and precise estimates of constituent load.     

Kriging and Bilinear Methods for Estimating Spatial Pattern of Atmospheric Pollutants

Two statistical methods to reconstruct the spatial pattern ofenvironmental data are compared: the Kriging and the Bilinear.The comparison is performed by evaluating two differentcharacteristics of the methods: the interpolation and the estimation. The Kriging method is demonstrated to be better thanthe Bilinear regarding all the statistics studied, although thedifferences are not very significant. Especially, the Kriging ismore efficient than the Bilinear in the reconstruction of peakvalues and it does not extrapolate local trends. The methods areapplied to a set of environmental data measured in an urban area.     

Comparison of two carbon analysis methods for monitoring diesel particulate levels in mines

Two carbon analysis methods are currently being applied to the occupational monitoring of diesel particulate matter. Both methods are based on thermal techniques for the determination of organic and elemental carbon. In Germany, method ZH 1/120.44 has been published. This method, or a variation of it, is being used for compliance measurements in several European countries, and a Comité Européen de Normalization Working Group was formed recently to address the establishment of a European measurement standard. In the USA, a 'thermal-optical' method has been published as Method 5040 by the National Institute for Occupational Safety and Health. As with ZH 1/120.44, organic and elemental carbon are determined through temperature and atmosphere control, but different instrumentation and analysis conditions are used. Although the two methods are similar in principle, they gave statistically different results in a previous interlaboratory comparison. Because different instruments and operating conditions are used, between-method differences can be expected in some cases. Reasonable agreement is expected when the sample contains no other (i.e., non-diesel) sources of carbonaceous particulate and the organic fraction is essentially removed below about 500 degrees C. Airborne particulate samples from some mines may meet these criteria. Comparison data on samples from mines are important because the methods are being applied in this workplace for occupational monitoring and epidemiological studies. In this paper, results of a recent comparison on samples collected in a Canadian mine are reported. As seen in a previous comparison, there was good agreement between the total carbon results found by the two methods, with ZH 1/120.44 giving about 6% less carbon than Method 5040. Differences in the organic and elemental carbon results were again seen, but they were much smaller than those obtained in the previous comparison. The relatively small differences in the split between organic and elemental carbon are attributed to the different thermal programs used.     

The geostatistic-based spatial distribution variations of soil salts under long-term wastewater irrigation

The purpose of this study was to determine and evaluate the spatial changes in soil salinity by using geostatistical methods. The study focused on the suburb area of Beijing, where urban development led to water shortage and accelerated wastewater reuse to farm irrigation for more than 30 years. The data were then processed by GIS using three different interpolation techniques of ordinary kriging (OK), disjunctive kriging (DK), and universal kriging (UK). The normality test and overall trend analysis were applied for each interpolation technique to select the best fitted model for soil parameters. Results showed that OK was suitable for soil sodium adsorption ratio (SAR) and Na⁺ interpolation; UK was suitable for soil Cl^? and pH; DK was suitable for soil Ca²⁺. The nugget-to-sill ratio was applied to evaluate the effects of structural and stochastic factors. The maps showed that the areas of non-saline soil and slight salinity soil accounted for 6.39 and 93.61 %, respectively. The spatial distribution and accumulation of soil salt were significantly affected by the irrigation probabilities and drainage situation under long-term wastewater irrigation.     

An Integrated Multicriteria Decision Analysis System for Reducing Air Emissions from Mining Process

The selection of a best alternative method to minimize air pollution and energy consumption for mine sites is a critical task because it encompasses evaluation of different techniques. The aim of this paper is to select most suitable technology for mining system which helps in reducing air pollution and carbon footprints by implementing the multicriteria decision analysis (MCDA) method. The existing methods or frameworks in the mining sector, which have been used in the past to select the sustainable solution, are lacking aid of MCDA, and there is a need to contribute more in this field with a promising decision system. The MCDA method is applied as a probabilistic integrated approach for a mine site in Canada. The analysis involves processing inputs to the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method which assists in identifying the alternatives, defining the criteria, and thus outranking of the final choice. Moreover, criteria weighting has been determined using analytical hierarchical process (AHP) method. Three categories: reduction of dust/fugitive emission control strategies, reduction in fuel consumption to minimize carbon footprint, and cyanide destruction methods are selected. The probability distributions of criteria weights and output flows are defined by performing uncertainty analysis using the Monte Carlo simulation (MCS). The sensitivity analysis is conducted using Spearman’s rank correlation method and walking criteria weights. The results indicate that the integrated framework provides a reliable way of selecting air pollution control solutions and help in quantifying the impact of different criteria for the selected alternatives.

     

Groundwater depth and elevation interpolation by kriging methods in Mohr Basin of Fars province in Iran

Prediction of groundwater depth and elevation is important in quantitative water management especially in arid areas. There are several basins in southwest of Iran, in Zagross Mountain, in which the water wells are distributed along a narrow elliptic ring band around the region. To find the most applicable interpolation method, both of the groundwater depth and elevation are predicted by different kriging methods. It is found that the groundwater elevation and depth can be predicted by different methods. Furthermore, it is found that the methods in which the trend is eliminated predicted the groundwater elevation and depth in central part of the region is with less standard error. Furthermore, the methods with no trend elimination, predicted the groundwater depths with less error near the water wells. Dividing the area to hydro-geologically homogeneous sub-areas improved the interpolation precision.     

Optimum spatial analysis of monitoring data on temperature,salinity and nutrient concentrations in the Baltic Proper

In spite of the large number of monitoring data on hydrography and nutrients collected from the Baltic Sea, it is still difficult to describe large-scale distribution patterns of these variables. We therefore suggest a stochastic approach that allows the spatial reconstruction of the fields for the entire sea. The Baltic Sea monitoring data on temperature, salinity and nutrient concentrations from the years 1972–1991 are each divided into twenty data sets: five regions, times four seasons. The spatial regions are the Southern Baltic Proper, the Northern and Central Baltic Proper, both above and below halocline, and the region of the Gulf of Finland and the Gulf of Riga. The four seasons consist of three-month periods: January–March (winter), April–June (spring), July–September (summer) and October–December (fall). Each monthly subset of a regional and seasonal data set is modeled as a sample out of a monthly realization of a random field. The data sets are decomposed into mean and fluctuational components. The mean is determined as an average over the space cells with dimensions of standard sampling depth intervals vertically, 10 in meridional (south-north), 20 in zonal (west-east) directions and over five-year periods in time. The fluctuation fields are considered second-order stationary, homogeneous and horizontally isotropic. Estimated horizontal (surface) and vertical (depth) components of the spatial correlations are approximated by Gaussian functions. The correlation scales for the fields of the Baltic Proper are mostly larger than 100 nautical miles horizontally and 40 m vertically and their dependence on the sea region or season is relatively weak. The most probable noise-to-signal ratio values of the data lie in the interval 0.6 to 1.2. The estimated correlation functions and noise-to-signal ratios allow the optimum analysis technique to assess the correctness of each datum of a sample on the background of the field statistics. The outliers of each monthly sample are excluded from the analysis. The observed fluctuations are interpolated into locations with missing data by an optimum interpolation procedure. The discrete cell-and-five-year mean values are interpolated by a different, piece-wise linear technique. Since the data number for the mean interpolation considerably exceeds the data number for the fluctuation interpolation, the interpolation errors for the mean are assumed negligible compared to the interpolation errors for the fluctuation. The sums of the mean and fluctuation, interpolated into the withheld observation points, are compared to the actually observed values and to some other linear interpolation estimates. In all test cases the optimum interpolation procedure performs the best.     

全国近岸海域水质空间插值算法精度分析

空间插值技术对近海环境数据的分析和表征具有广泛应用价值,但目前针对各类模型适用性和精度比较的研究很少。基于2009年全国近岸海域299个站位的水质监测数据,利用交叉检验法,对3种插值算法13种模型的空间拟合结果进行分析和验证。结果表明,13种模型的模拟值与实测值显著相关(r=0.643~0.750,P0.01,n=299),说明其均适用于近岸海域水质的空间模拟;综合比较模拟结果与实测结果的相关系数、平均误差、平均绝对误差、均方差及偏离程度,排序为IDW(p2)Spline-TensionIDW(p3)IDW(p4)IDW(p1)IDW(p5)IDW(p6)Kriging-GuassianKriging-Spherical,Kriging-CircularKriging-LinearKriging-ExponentialSpline-Regularized,提出IDW(p2)是精度最优的空间插值模型。     

Measuring differences between overlapping but unpaired spatial surveys using a geographic information system

To compare the results from two overlapping but unpaired synoptic surveys, a method using a geographic information system is proposed. The new method uses a GIS to create (i) minimum convex polygons (MCP) enclosing each set of survey data and (ii) Voronoi tesselations assigning area weights to each datum. The pairs of MCP and Voronoi maps are overlaid to produce a mosaic of polygons each with one datum assigned from each survey. The differences between the pairs weighted by the polygon areas provides the basis for statistical testing. Area-weighted means and variances of paired differences are computed and a z-statistic measures the significance of differences for the whole intersection area. A cross-product autocorrelation statistic provides an assessment of the spatial distribution of differences. Alternate, conventional methods are compared with the new method: analysis of variance (ANOVA), analysis of covariance (ANCOVA), and contour overlays. The four methods were applied to the comparison of two macrobenthic surveys conducted in Lake Erie. Measures for five different taxa were examined. The ANOVA and ANCOVA methods found many significant differences between surveys. Both methods were judged inappropriate as sampling data are not expected to be drawn from normally-distributed populations in spatial surveys. Differences between surveys were detected but were difficult to assess using contour overlays. The new method only found a significant difference in one taxon, an expected difference as members of that taxon were absent from most sites in one survey.     

Using similarity measures in benthic impact assessments

Eleven similarity measures were used to assess the impacts of clam digging on the infauna of one intertidal mud-flat. When the similarity matrices were clustered using a divisive polythetic algorithm two different conclusions were possible from the dendograms produced. Dendograms generated by 3 coefficients indicated digging had an impact on areas dug more than once, while the 8 others did not. In addition, existing data from an impact analysis of a power plant effluent were re-analyzed using 5 different coefficients. In this case, three of the five coefficients gave results which indicated that the benthic community at the effluent pipe was different from that present at the control. Two coefficients gave the opposite conclusion — the populations were not different.These results indicate that the objectivity of cluster analysis, as it has come to be used in impact studies, is only apparent, not real. Too many subjective choices are made in selecting algorithms and methods of interpretation. Objective criteria need to be developed for the choices made, based on the intrinsic and ecological properties of the quantitative methods. Some criteria that have been developed are discussed, and used to determine which coefficients are best suited for the two data sets analyzed. To reduce the number of subjective choices made an analysis of variance of the similarity matrix is presented as an alternative to clustering.     

Comparison of Point Estimation Techniques in the Spatial Analysis of Radium-226, Radium-228 and Potassium-40 in Soil

Many environmental surveys require the implementation of estimation techniques to determine the spatial distribution of the variable being investigated. Traditional methods of interpolation and estimation, for example, inverse distance squared and triangulation often ignore features of the data set such as anisotropy which may have a significant impact on the quality of the estimates produced. Geostatistical techniques may offer an improved method of estimation by modelling the spatial continuity of the variable using semi-variogram analysis. The theoretical model fitted to the semi-variogram is then used in the assignation of weighting factors to the samples surrounding the location to be estimated. This paper outlines the results of a comparison between three common estimation methods, polygonal, triangulation and inverse distance squared and a geostatistical method, in the estimation of soil radionuclide activities. The geostatistical estimation method known as kriging performed best over a range of parameters used to test the performance of the methods. Kriging exhibited the best correlation between actual and estimated values, the narrowest error distribution and the lowest overall estimation error. Polygonal estimation was best at reproducing the data set distribution. Conditional bias was evident in all the methods, low values being over-estimated and high values being under-estimated.     

土壤重金属分析方法间检测结果一致性判断方法——数据对t值检验法的改进与完善

不同分析方法测定结果间的比较,或者说如何判断两种分析方法测定结果的一致性,是检测实验室经常遇到的实际问题。其应用场景包括方法标准验证实验、新方法与经典方法间的比对,特别是在缺少标准样品、测试数据不满足t值检验的条件下,选用合适的判据尤其重要。以采用单波长激发型能量色散X射线荧光光谱法、电感耦合等离子体质谱法、波长色散X射线荧光光谱法测定土壤和水系沉积物样品中Cd、As、Pb、Cu、Zn、Ni、Cr、Co、V、Mn、Mo、Sb等重金属含量所得数据为例,探讨了《环境监测分析方法标准制订技术导则》(HJ 168—2020)附录B中规定的"数据对t值检验法"的不足及改进办法,即用数据对相对偏差和再现性限合格率作为判据,判断分析方法测定结果的一致性。对一组验证样品分别用两种分析方法进行了测定,在数据对t值检验的基础上,取各元素测定结果的相对偏差与相关限值进行比较,或者取两者的差值与基准分析方法标准规定的再现性限进行比较,利用合格率大小协助判断了两分析方法测定结果的一致性。研究结果表明,此方法符合日常质控技术规范中关于精密度的规定,实用性强,是一条将数理统计原理与专业知识相结合的合理技术路线。     

Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches

Hyperspectral data can provide prediction of physical and chemical vegetation properties, but data handling, analysis, and interpretation still limit their use. In this study, different methods for selecting variables were compared for the analysis of on-the-ground hyperspectral signatures of wheat grown under a wide range of nitrogen supplies. Spectral signatures were recorded at the end of stem elongation, booting, and heading stages in 100 georeferenced locations, using a 512-channel portable spectroradiometer operating in the 325–1075-nm range. The following procedures were compared: (i) a heuristic combined approach including lambda-lambda R² (LL R²) model, principal component analysis (PCA), and stepwise discriminant analysis (SDA); (ii) variable importance for projection (VIP) statistics derived from partial least square (PLS) regression (PLS-VIP); and (iii) multiple linear regression (MLR) analysis through maximum R-square improvement (MAXR) and stepwise algorithms. The discriminating capability of selected wavelengths was evaluated by canonical discriminant analysis. Leaf-nitrogen concentration was quantified on samples collected at the same locations and dates and used as response variable in regressive methods. The different methods resulted in differences in the number and position of the selected wavebands. Bands extracted through regressive methods were mostly related to response variable, as shown by the importance of the visible region for PLS and stepwise. Band selection techniques can be extremely useful not only to improve the power of predictive models but also for data interpretation or sensor design.     

紫外分光光度法测定地下水系统中油类的标准油选定

研究了采用含油污水中油类作为分析受其污染的地下水系统中油类的标准油可行性 ,紫外吸收光谱图和色质联机分析表明 ,污水、地下水、土壤中油类不仅主要成分相似 ,其相对含量也相同 ,证明了标准油选定的正确性。试验同时表明地下水系统中油类的污染正是目前仍在排放的含油污水引起的。     

Modelling geosmin concentrations in three sources of raw water in Quebec, Canada

The presence of off-flavour compounds such as geosmin, often found in raw water, significantly reduces the organoleptic quality of distributed water and diverts the consumer from its use. To adapt water treatment processes to eliminate these compounds, it is necessary to be able to identify them quickly. Routine analysis could be considered a solution, but it is expensive and delays associated with obtaining the results of analysis are often important, thereby constituting a serious disadvantage. The development of decision-making tools such as predictive models seems to be an economic and feasible solution to counterbalance the limitations of analytical methods. Among these tools, multi-linear regression and principal component regression are easy to implement. However, due to certain disadvantages inherent in these methods (multicollinearity or non-linearity of the processes), the use of emergent models involving artificial neurons networks such as multi-layer perceptron could prove to be an interesting alternative. In a previous paper (Parinet et al., Water Res 44: 5847-5856, 2010), the possible parameters that affect the variability of taste and odour compounds were investigated using principal component analysis. In the present study, we expand the research by comparing the performance of three tools using different modelling scenarios (multi-linear regression, principal component regression and multi-layer perceptron) to model geosmin in drinking water sources using 38 microbiological and physicochemical parameters. Three very different sources of water, in terms of quality, were selected for the study. These sources supply drinking water to the Québec City area (Canada) and its vicinity, and were monitored three times per month over a 1-year period. Seven different modelling methods were tested for predicting geosmin in these sources. The comparison of the seven different models showed that simple models based on multi-linear regression provide sufficient predictive capacity with performance levels comparable to those obtained with artificial neural networks. The multi-linear regression model (R ²?=?0.657, <0.001) used only four variables (phaeophytin, sum of green algae, chlorophyll-a and potential Redox) in comparison with ten variables (potassium, heterotrophic bacteria, organic nitrogen, total nitrogen, phaeophytin, total organic carbon, sum of green algae, potential Redox, UV absorbance at 254 nm and atypical bacteria) for the best model obtained with artificial neural networks (R ²?=?0.843).     

Thiram: degradation, applications and analytical methods

In this review a brief introduction to thiram (tetramethylthiuram disulfide; TMTD) pesticide has been given along with other applications. All the important methods available are systematically arranged and are listed under various techniques. Some of these methods have been applied for the determination of thiram in commercial formulations, synthetic mixtures in grains, vegetables and fruits. A comparison of different methods is the salient feature of this review.